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@@ -9,11 +9,12 @@ from sklearn.model_selection import train_test_split
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from sklearn.metrics import mean_absolute_error, r2_score, mean_absolute_percentage_error
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from config import set_config
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-from utils import read_from_pickle, write_to_pickle, data_normalization, request_post, filter_video_status
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+from utils import read_from_pickle, write_to_pickle, data_normalization, \
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+ request_post, filter_video_status, update_video_w_h_rate
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from log import Log
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from db_helper import RedisHelper, MysqlHelper
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-config_ = set_config()
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+config_, env = set_config()
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log_ = Log()
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@@ -124,7 +125,7 @@ def pack_result_to_csv(filename, sort_columns=None, filepath=config_.DATA_DIR_PA
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:param sort_columns: 指定排序列名列名,type-list, 默认为None
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:param filepath: csv文件存放路径,默认为config_.DATA_DIR_PATH
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:param ascending: 是否按指定列的数组升序排列,默认为True,即升序排列
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- :param data: 数据
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+ :param data: 数据, type-dict
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:return: None
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"""
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if not os.path.exists(filepath):
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@@ -136,6 +137,26 @@ def pack_result_to_csv(filename, sort_columns=None, filepath=config_.DATA_DIR_PA
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df.to_csv(file, index=False)
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+def pack_list_result_to_csv(filename, data, columns=None, sort_columns=None, filepath=config_.DATA_DIR_PATH, ascending=True):
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+ """
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+ 打包数据并存入csv, 数据为字典列表
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+ :param filename: csv文件名
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+ :param data: 数据,type-list [{}, {},...]
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+ :param columns: 列名顺序
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+ :param sort_columns: 指定排序列名列名,type-list, 默认为None
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+ :param filepath: csv文件存放路径,默认为config_.DATA_DIR_PATH
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+ :param ascending: 是否按指定列的数组升序排列,默认为True,即升序排列
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+ :return: None
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+ """
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+ if not os.path.exists(filepath):
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+ os.makedirs(filepath)
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+ file = os.path.join(filepath, filename)
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+ df = pd.DataFrame(data=data)
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+ if sort_columns:
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+ df = df.sort_values(by=sort_columns, ascending=ascending)
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+ df.to_csv(file, index=False, columns=columns)
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+
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+
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def predict():
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"""预测"""
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# 读取预测数据并进行清洗
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@@ -146,48 +167,74 @@ def predict():
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# 预测
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y_ = model.predict(x)
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log_.info('predict finished!')
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+
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# 将结果进行归一化到[0, 100]
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normal_y_ = data_normalization(list(y_))
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log_.info('normalization finished!')
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+
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+ # 按照normal_y_降序排序
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+ predict_data = []
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+ for i, video_id in enumerate(video_ids):
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+ data = {'video_id': video_id, 'normal_y_': normal_y_[i], 'y_': y_[i], 'y': y[i]}
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+ predict_data.append(data)
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+ predict_data_sorted = sorted(predict_data, key=lambda temp: temp['normal_y_'], reverse=True)
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+
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+ # 按照排序,从100以固定差值做等差递减,以该值作为rovScore
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+ predict_result = []
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+ redis_data = {}
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+ json_data = []
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+ video_id_list = []
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+ for j, item in enumerate(predict_data_sorted):
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+ video_id = int(item['video_id'])
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+ rov_score = 100 - j * config_.ROV_SCORE_D
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+ item['rov_score'] = rov_score
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+ predict_result.append(item)
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+ redis_data[video_id] = rov_score
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+ json_data.append({'videoId': video_id, 'rovScore': rov_score})
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+ video_id_list.append(video_id)
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+
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# 打包预测结果存入csv
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- predict_data = {'normal_y_': normal_y_, 'y_': y_, 'y': y, 'video_ids': video_ids}
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predict_result_filename = 'predict.csv'
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- pack_result_to_csv(filename=predict_result_filename, sort_columns=['normal_y_'], ascending=False, **predict_data)
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+ pack_list_result_to_csv(filename=predict_result_filename,
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+ data=predict_result,
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+ columns=['video_id', 'rov_score', 'normal_y_', 'y_', 'y'],
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+ sort_columns=['rov_score'],
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+ ascending=False)
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+
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# 上传redis
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- redis_data = {}
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- json_data = []
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- for i in range(len(video_ids)):
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- redis_data[video_ids[i]] = normal_y_[i]
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- json_data.append({'videoId': video_ids[i], 'rovScore': normal_y_[i]})
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key_name = config_.RECALL_KEY_NAME_PREFIX + time.strftime('%Y%m%d')
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redis_helper = RedisHelper()
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redis_helper.add_data_with_zset(key_name=key_name, data=redis_data)
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log_.info('data to redis finished!')
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+
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+ # 清空修改ROV的视频数据
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+ redis_helper.del_keys(key_name=config_.UPDATE_ROV_KEY_NAME)
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+
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# 通知后端更新数据
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+ log_.info('json_data count = {}'.format(len(json_data)))
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result = request_post(request_url=config_.NOTIFY_BACKEND_UPDATE_ROV_SCORE_URL, request_data={'videos': json_data})
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if result['code'] == 0:
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log_.info('notify backend success!')
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else:
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log_.error('notify backend fail!')
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+ # 更新视频的宽高比数据
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+ if video_id_list:
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+ update_video_w_h_rate(video_ids=video_id_list,
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+ key_name=config_.W_H_RATE_UP_1_VIDEO_LIST_KEY_NAME['rov_recall'])
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+ log_.info('update video w_h_rate to redis finished!')
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+
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def predict_test():
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"""测试环境数据生成"""
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# 获取测试环境中最近发布的40000条视频
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- mysql_info = {
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- 'host': 'rm-bp1k5853td1r25g3n690.mysql.rds.aliyuncs.com',
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- 'port': 3306,
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- 'user': 'wx2016_longvideo',
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- 'password': 'wx2016_longvideoP@assword1234',
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- 'db': 'longvideo'
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- }
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sql = "SELECT id FROM wx_video ORDER BY id DESC LIMIT 40000;"
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- mysql_helper = MysqlHelper(mysql_info=mysql_info)
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+ mysql_helper = MysqlHelper()
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data = mysql_helper.get_data(sql=sql)
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video_ids = [video[0] for video in data]
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# 视频状态过滤
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filtered_videos = filter_video_status(video_ids)
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- log_.info('filtered_videos nums={}'.format(len(filtered_videos)))
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+ log_.info('filtered_videos count = {}'.format(len(filtered_videos)))
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# 随机生成 0-100 数作为分数
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redis_data = {}
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json_data = []
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@@ -195,17 +242,25 @@ def predict_test():
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score = random.uniform(0, 100)
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redis_data[video_id] = score
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json_data.append({'videoId': video_id, 'rovScore': score})
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+ log_.info('json_data count = {}'.format(len(json_data)))
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# 上传Redis
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redis_helper = RedisHelper()
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key_name = config_.RECALL_KEY_NAME_PREFIX + time.strftime('%Y%m%d')
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redis_helper.add_data_with_zset(key_name=key_name, data=redis_data)
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log_.info('test data to redis finished!')
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+ # 清空修改ROV的视频数据
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+ redis_helper.del_keys(key_name=config_.UPDATE_ROV_KEY_NAME)
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# 通知后端更新数据
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result = request_post(request_url=config_.NOTIFY_BACKEND_UPDATE_ROV_SCORE_URL, request_data={'videos': json_data})
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if result['code'] == 0:
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log_.info('notify backend success!')
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else:
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log_.error('notify backend fail!')
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+ # 更新视频的宽高比数据
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+ if filtered_videos:
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+ update_video_w_h_rate(video_ids=filtered_videos,
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+ key_name=config_.W_H_RATE_UP_1_VIDEO_LIST_KEY_NAME['rov_recall'])
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+ log_.info('update video w_h_rate to redis finished!')
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if __name__ == '__main__':
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@@ -220,6 +275,11 @@ if __name__ == '__main__':
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log_.info('rov model predict start...')
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predict_start = time.time()
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- predict()
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+ if env in ['dev', 'test']:
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+ predict_test()
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+ elif env in ['pre', 'pro']:
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+ predict()
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+ else:
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+ log_.error('env error')
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predict_end = time.time()
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log_.info('rov model predict end, execute time = {}ms'.format((predict_end - predict_start)*1000))
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